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   <div id="projectname">Reranker Framework (ReFr)
   
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   <div id="projectbrief">Reranking framework for structure prediction and discriminative language modeling</div>
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<a href="training-vector-set_8_h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">// Copyright 2012, Google Inc.</span>
<a name="l00002"></a>00002 <span class="comment">// All rights reserved.</span>
<a name="l00003"></a>00003 <span class="comment">// </span>
<a name="l00004"></a>00004 <span class="comment">// Redistribution and use in source and binary forms, with or without</span>
<a name="l00005"></a>00005 <span class="comment">// modification, are permitted provided that the following conditions are</span>
<a name="l00006"></a>00006 <span class="comment">// met:</span>
<a name="l00007"></a>00007 <span class="comment">// </span>
<a name="l00008"></a>00008 <span class="comment">//   * Redistributions of source code must retain the above copyright</span>
<a name="l00009"></a>00009 <span class="comment">//     notice, this list of conditions and the following disclaimer.</span>
<a name="l00010"></a>00010 <span class="comment">//   * Redistributions in binary form must reproduce the above</span>
<a name="l00011"></a>00011 <span class="comment">//     copyright notice, this list of conditions and the following disclaimer</span>
<a name="l00012"></a>00012 <span class="comment">//     in the documentation and/or other materials provided with the</span>
<a name="l00013"></a>00013 <span class="comment">//     distribution.</span>
<a name="l00014"></a>00014 <span class="comment">//   * Neither the name of Google Inc. nor the names of its</span>
<a name="l00015"></a>00015 <span class="comment">//     contributors may be used to endorse or promote products derived from</span>
<a name="l00016"></a>00016 <span class="comment">//     this software without specific prior written permission.</span>
<a name="l00017"></a>00017 <span class="comment">//</span>
<a name="l00018"></a>00018 <span class="comment">// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span>
<a name="l00019"></a>00019 <span class="comment">// &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span>
<a name="l00020"></a>00020 <span class="comment">// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR</span>
<a name="l00021"></a>00021 <span class="comment">// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT</span>
<a name="l00022"></a>00022 <span class="comment">// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,</span>
<a name="l00023"></a>00023 <span class="comment">// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT</span>
<a name="l00024"></a>00024 <span class="comment">// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,           </span>
<a name="l00025"></a>00025 <span class="comment">// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY           </span>
<a name="l00026"></a>00026 <span class="comment">// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT</span>
<a name="l00027"></a>00027 <span class="comment">// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE</span>
<a name="l00028"></a>00028 <span class="comment">// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</span>
<a name="l00029"></a>00029 <span class="comment">// -----------------------------------------------------------------------------</span>
<a name="l00030"></a>00030 <span class="comment">//</span>
<a name="l00031"></a>00031 <span class="comment">//</span>
<a name="l00035"></a>00035 <span class="comment"></span>
<a name="l00036"></a>00036 <span class="preprocessor">#ifndef RERANKER_TRAINING_VECTOR_SET_H_</span>
<a name="l00037"></a>00037 <span class="preprocessor"></span><span class="preprocessor">#define RERANKER_TRAINING_VECTOR_SET_H_</span>
<a name="l00038"></a>00038 <span class="preprocessor"></span>
<a name="l00039"></a>00039 <span class="preprocessor">#include &lt;iostream&gt;</span>
<a name="l00040"></a>00040 <span class="preprocessor">#include &lt;tr1/unordered_map&gt;</span>
<a name="l00041"></a>00041 <span class="preprocessor">#include &lt;tr1/unordered_set&gt;</span>
<a name="l00042"></a>00042 
<a name="l00043"></a>00043 <span class="preprocessor">#include &quot;<a class="code" href="training-time_8_h.html" title="Provides the reranker::Time class, which holds the three notions of training time: current epoch...">training-time.H</a>&quot;</span>
<a name="l00044"></a>00044 <span class="preprocessor">#include &quot;<a class="code" href="feature-vector_8_h.html" title="Defines the reranker::FeatureVector class, which, as it happens, is used to store feature vectors...">feature-vector.H</a>&quot;</span>
<a name="l00045"></a>00045 
<a name="l00046"></a>00046 <span class="keyword">namespace </span>reranker {
<a name="l00047"></a>00047 
<a name="l00048"></a>00048 <span class="keyword">using</span> std::cerr;
<a name="l00049"></a>00049 <span class="keyword">using</span> std::endl;
<a name="l00050"></a>00050 <span class="keyword">using</span> std::tr1::unordered_map;
<a name="l00051"></a>00051 <span class="keyword">using</span> std::tr1::unordered_set;
<a name="l00052"></a>00052 
<a name="l00059"></a><a class="code" href="classreranker_1_1_training_vector_set.html">00059</a> <span class="keyword">class </span><a class="code" href="classreranker_1_1_training_vector_set.html" title="A class to hold the several feature vectors needed during training (especially for the perceptron fam...">TrainingVectorSet</a> {
<a name="l00060"></a>00060  <span class="keyword">public</span>:
<a name="l00061"></a><a class="code" href="classreranker_1_1_training_vector_set.html#af5282652b1f0ee34453543af00f8cee1">00061</a>   <span class="keyword">friend</span> <span class="keyword">class </span><a class="code" href="classreranker_1_1_perceptron_model_proto_reader.html" title="A class to construct a PerceptronModel from a ModelMessage instance.">PerceptronModelProtoReader</a>;
<a name="l00063"></a><a class="code" href="classreranker_1_1_training_vector_set.html#aed20b78e2d1864aaeb338f7d2a5c1791">00063</a>   <a class="code" href="classreranker_1_1_training_vector_set.html#aed20b78e2d1864aaeb338f7d2a5c1791" title="Constructs a new set of feature vectors (models) for use during training.">TrainingVectorSet</a>() { }
<a name="l00065"></a><a class="code" href="classreranker_1_1_training_vector_set.html#acba29d899d0d74be86ba31dd6b9d8810">00065</a>   <span class="keyword">virtual</span> <a class="code" href="classreranker_1_1_training_vector_set.html#acba29d899d0d74be86ba31dd6b9d8810" title="Destroys this instance.">~TrainingVectorSet</a>() { }
<a name="l00066"></a>00066 
<a name="l00067"></a>00067   <span class="comment">// accessors</span>
<a name="l00068"></a>00068 
<a name="l00072"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a49b3808a509cf2e46edfdc4df6fbd62d">00072</a>   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int,double&gt;</a> &amp;<a class="code" href="classreranker_1_1_training_vector_set.html#a49b3808a509cf2e46edfdc4df6fbd62d" title="Returns the &quot;raw&quot; feature weights computed during training.">weights</a>()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> weights_; }
<a name="l00074"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7">00074</a>   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int,double&gt;</a> &amp;<a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7" title="Returns the feature vector corresponding to the averaged perceptron.">average_weights</a>()<span class="keyword"> const </span>{
<a name="l00075"></a>00075     <span class="keywordflow">return</span> average_weights_;
<a name="l00076"></a>00076   }
<a name="l00077"></a>00077 
<a name="l00083"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a98dd763d1e1b3b980e07091488f9b422">00083</a>   <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int,double&gt;</a> &amp;<a class="code" href="classreranker_1_1_training_vector_set.html#a98dd763d1e1b3b980e07091488f9b422" title="Returns either the raw or averaged feature vector, depending on the argument.">GetModel</a>(<span class="keywordtype">bool</span> raw)<span class="keyword"> const </span>{
<a name="l00084"></a>00084     <span class="keywordflow">return</span> raw ? <a class="code" href="classreranker_1_1_training_vector_set.html#a49b3808a509cf2e46edfdc4df6fbd62d" title="Returns the &quot;raw&quot; feature weights computed during training.">weights</a>() : <a class="code" href="classreranker_1_1_training_vector_set.html#a21e449676837c2a0aa03a6749bc9b6f7" title="Returns the feature vector corresponding to the averaged perceptron.">average_weights</a>();
<a name="l00085"></a>00085   }
<a name="l00086"></a>00086 
<a name="l00087"></a>00087   <span class="comment">// mutators</span>
<a name="l00088"></a>00088 
<a name="l00103"></a>00103   <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Collection&gt;
<a name="l00104"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a362f24bcf6aac291c7e6e74a38815df9">00104</a>   <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_training_vector_set.html#a362f24bcf6aac291c7e6e74a38815df9" title="Increments the weights for the specified collection of features.">UpdateWeights</a>(<span class="keyword">const</span> <a class="code" href="classreranker_1_1_time.html" title="A simple class to hold the three notions of time during training: the current epoch, the current time index within the current epoch, and the absolute time index.">Time</a> &amp;time,
<a name="l00105"></a>00105                      <span class="keyword">const</span> Collection &amp;feature_uids,
<a name="l00106"></a>00106                      <span class="keyword">const</span> <a class="code" href="classreranker_1_1_feature_vector.html">FeatureVector&lt;int,double&gt;</a> &amp;feature_vector,
<a name="l00107"></a>00107                      <span class="keywordtype">double</span> scalar) {
<a name="l00108"></a>00108     weights_.<a class="code" href="classreranker_1_1_feature_vector.html#aa732c2a9d4b249b652f881eb57112e1f" title="Modifies this vector so that it equals this vector plus the scaled specified subvector.">AddScaledSubvector</a>(feature_uids, feature_vector, scalar);
<a name="l00109"></a>00109   }
<a name="l00110"></a>00110 
<a name="l00114"></a>00114   <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Collection&gt;
<a name="l00115"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a157d68898da5d72f39d36c928c152693">00115</a>   <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_training_vector_set.html#a157d68898da5d72f39d36c928c152693" title="Updates the feature averages the specified pair of feature uid collections, one for a gold reference ...">UpdateGoldAndCandidateFeatureAverages</a>(<span class="keyword">const</span> <a class="code" href="classreranker_1_1_time.html" title="A simple class to hold the three notions of time during training: the current epoch, the current time index within the current epoch, and the absolute time index.">Time</a> &amp;time,
<a name="l00116"></a>00116                                              <span class="keyword">const</span> Collection &amp;
<a name="l00117"></a>00117                                                gold_feature_uids,
<a name="l00118"></a>00118                                              <span class="keyword">const</span> Collection &amp;
<a name="l00119"></a>00119                                                candidate_feature_uids) {
<a name="l00120"></a>00120     UpdateFeatureAverages(time,
<a name="l00121"></a>00121                           gold_feature_uids.begin(),
<a name="l00122"></a>00122                           gold_feature_uids.end());
<a name="l00123"></a>00123     UpdateFeatureAverages(time,
<a name="l00124"></a>00124                           candidate_feature_uids.begin(),
<a name="l00125"></a>00125                           candidate_feature_uids.end());
<a name="l00126"></a>00126   }
<a name="l00127"></a>00127 
<a name="l00128"></a><a class="code" href="classreranker_1_1_training_vector_set.html#aaf0c6eb3e55fe11773d23589d98187d5">00128</a>   <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_training_vector_set.html#aaf0c6eb3e55fe11773d23589d98187d5">UpdateAllFeatureAverages</a>(<span class="keyword">const</span> <a class="code" href="classreranker_1_1_time.html" title="A simple class to hold the three notions of time during training: the current epoch, the current time index within the current epoch, and the absolute time index.">Time</a> &amp;time) {
<a name="l00129"></a>00129     <span class="comment">// Get set union of non-zero feature weights and average feature weights.</span>
<a name="l00130"></a>00130     unordered_set&lt;int&gt; uids;
<a name="l00131"></a>00131     weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a87bccd3acb055b98a674e6ed7d29af4b" title="Inserts the uid&#39;s of features with non-zero weights into the specified set.">GetNonZeroFeatures</a>(uids);
<a name="l00132"></a>00132     average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a87bccd3acb055b98a674e6ed7d29af4b" title="Inserts the uid&#39;s of features with non-zero weights into the specified set.">GetNonZeroFeatures</a>(uids);
<a name="l00133"></a>00133     UpdateFeatureAverages(time, uids.begin(), uids.end());
<a name="l00134"></a>00134   }
<a name="l00135"></a>00135 
<a name="l00136"></a><a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">00136</a>   <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(<span class="keyword">const</span> unordered_map&lt;int, int&gt; &amp;old_to_new_uids) {
<a name="l00137"></a>00137     <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(old_to_new_uids, weights_);
<a name="l00138"></a>00138     <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(old_to_new_uids, average_weights_);
<a name="l00139"></a>00139     <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(old_to_new_uids, weight_sums_);
<a name="l00140"></a>00140     <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(old_to_new_uids, last_update_indices_);
<a name="l00141"></a>00141   }
<a name="l00142"></a>00142 
<a name="l00143"></a>00143   <span class="comment">// I/O methods</span>
<a name="l00144"></a><a class="code" href="classreranker_1_1_training_vector_set.html#a4678b948974965a1d7a15f06b4bfd925">00144</a>   <span class="keyword">friend</span> ostream &amp;<a class="code" href="classreranker_1_1_training_vector_set.html#a4678b948974965a1d7a15f06b4bfd925">operator&lt;&lt;</a>(ostream &amp;os, <span class="keyword">const</span> <a class="code" href="classreranker_1_1_training_vector_set.html" title="A class to hold the several feature vectors needed during training (especially for the perceptron fam...">TrainingVectorSet</a> &amp;tvs) {
<a name="l00145"></a>00145     os &lt;&lt; <span class="stringliteral">&quot;weights: &quot;</span> &lt;&lt; tvs.weights_ &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>
<a name="l00146"></a>00146        &lt;&lt; <span class="stringliteral">&quot;average weights: &quot;</span> &lt;&lt; tvs.average_weights_ &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>
<a name="l00147"></a>00147        &lt;&lt; <span class="stringliteral">&quot;weight sums: &quot;</span> &lt;&lt; tvs.weight_sums_ &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>
<a name="l00148"></a>00148        &lt;&lt; <span class="stringliteral">&quot;last update indices: &quot;</span> &lt;&lt; tvs.last_update_indices_ &lt;&lt; <span class="stringliteral">&quot;\n&quot;</span>;
<a name="l00149"></a>00149     <span class="keywordflow">return</span> os;
<a name="l00150"></a>00150   }
<a name="l00151"></a>00151 
<a name="l00152"></a>00152  <span class="keyword">private</span>:
<a name="l00153"></a>00153 
<a name="l00154"></a>00154   <span class="keyword">template</span> &lt;<span class="keyword">typename</span> V&gt;
<a name="l00155"></a>00155   <span class="keywordtype">void</span> <a class="code" href="classreranker_1_1_training_vector_set.html#acd8ae561c7437253df45616432d3a683">RemapFeatureUids</a>(<span class="keyword">const</span> unordered_map&lt;int, int&gt; &amp;old_to_new_uids,
<a name="l00156"></a>00156                         <a class="code" href="classreranker_1_1_feature_vector.html" title="A class to represent a feature vector, where features are represented by unique identifiers, and feature values are represented by the template type.">FeatureVector&lt;int, V&gt;</a> &amp;vector) {
<a name="l00157"></a>00157     <a class="code" href="classreranker_1_1_feature_vector.html" title="A class to represent a feature vector, where features are represented by unique identifiers, and feature values are represented by the template type.">FeatureVector&lt;int, V&gt;</a> old_vector = vector;
<a name="l00158"></a>00158     vector.<a class="code" href="classreranker_1_1_feature_vector.html#a7082611256f0b6dc90ba4b38cc43e4ab" title="Sets all feature weights to zero and, because this is a sparse vector, clears all storage...">clear</a>();
<a name="l00159"></a>00159     <span class="keyword">typedef</span> <span class="keyword">typename</span> <a class="code" href="classreranker_1_1_feature_vector.html#a8dc4093ae778bffb30d2840002f02ba1" title="The type of const iterator for the feature-weight pairs in this vector.">FeatureVector&lt;int, V&gt;::const_iterator</a> fv_const_iterator;
<a name="l00160"></a>00160     <span class="keywordflow">for</span> (fv_const_iterator old_vector_it = old_vector.<a class="code" href="classreranker_1_1_feature_vector.html#ae0c185e646996daff25fcad9b224eda5" title="Returns a const iterator pointing to the first of the feature-value pairs of this feature vector...">begin</a>();
<a name="l00161"></a>00161          old_vector_it != old_vector.<a class="code" href="classreranker_1_1_feature_vector.html#a9f2539faf4e600c2f75b613763d49fcd" title="Returns a const iterator pointing to the end of the feature-value pairs of this feature vector...">end</a>();
<a name="l00162"></a>00162          ++old_vector_it) {
<a name="l00163"></a>00163       <span class="keywordtype">int</span> old_uid = old_vector_it-&gt;first;
<a name="l00164"></a>00164       unordered_map&lt;int, int&gt;::const_iterator old_to_new_uid_it =
<a name="l00165"></a>00165           old_to_new_uids.find(old_uid);
<a name="l00166"></a>00166       <span class="keywordflow">if</span> (old_to_new_uid_it != old_to_new_uids.end()) {
<a name="l00167"></a>00167         <span class="keywordtype">int</span> new_uid = old_to_new_uid_it-&gt;second;
<a name="l00168"></a>00168         V old_value = old_vector_it-&gt;second;
<a name="l00169"></a>00169         vector.<a class="code" href="classreranker_1_1_feature_vector.html#ad94d388ef81023981d6924a2020268d2" title="Sets the weight of the specified feature to the specified value.">SetWeight</a>(new_uid, old_value);
<a name="l00170"></a>00170       }
<a name="l00171"></a>00171     }
<a name="l00172"></a>00172   }
<a name="l00173"></a>00173 
<a name="l00177"></a>00177   <span class="keywordtype">void</span> UpdateAverage(<span class="keyword">const</span> Time &amp;time, <span class="keywordtype">int</span> uid) {
<a name="l00178"></a>00178     <span class="keywordtype">int</span> iterations_since_update =
<a name="l00179"></a>00179         time.absolute_index() - last_update_indices_.<a class="code" href="classreranker_1_1_feature_vector.html#a565133f9b5b31de43d07aea909631be5" title="Synonymous with GetWeight.">GetValue</a>(uid);
<a name="l00180"></a>00180     <span class="keywordflow">if</span> (iterations_since_update &lt;= 0) {
<a name="l00181"></a>00181       <span class="keywordflow">return</span>;
<a name="l00182"></a>00182     }
<a name="l00183"></a>00183     <span class="comment">// Add in perceptron values to weight sum.</span>
<a name="l00184"></a>00184     <span class="keywordtype">double</span> add_to_sum = iterations_since_update * weights_.<a class="code" href="classreranker_1_1_feature_vector.html#a0786ae9b9f6b579a80d8ae601edfd80a" title="Returns the weight of the feature with the specified uid, where crucially features not &quot;present&quot; in t...">GetWeight</a>(uid);
<a name="l00185"></a>00185     <span class="keywordtype">double</span> new_weight_sum = weight_sums_.<a class="code" href="classreranker_1_1_feature_vector.html#a541f26184a4407f62eb5dad8102cf375" title="Increments the weight of the specified feature by the specified amount.">IncrementWeight</a>(uid, add_to_sum);
<a name="l00186"></a>00186     average_weights_.<a class="code" href="classreranker_1_1_feature_vector.html#ad94d388ef81023981d6924a2020268d2" title="Sets the weight of the specified feature to the specified value.">SetWeight</a>(uid, new_weight_sum / time.absolute_index());
<a name="l00187"></a>00187     last_update_indices_.<a class="code" href="classreranker_1_1_feature_vector.html#ac0a4fceef14eba9efb471f5ec0476579" title="Synonym for SetWeight.">SetValue</a>(uid, time.absolute_index());
<a name="l00188"></a>00188   }
<a name="l00189"></a>00189 
<a name="l00191"></a>00191   <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Iterator&gt;
<a name="l00192"></a>00192   <span class="keywordtype">void</span> UpdateFeatureAverages(<span class="keyword">const</span> Time &amp;time,
<a name="l00193"></a>00193                              Iterator feature_uids_begin_it,
<a name="l00194"></a>00194                              Iterator feature_uids_end_it) {
<a name="l00195"></a>00195     <span class="keywordflow">for</span> (Iterator it = feature_uids_begin_it; it != feature_uids_end_it; ++it) {
<a name="l00196"></a>00196       UpdateAverage(time, *it);
<a name="l00197"></a>00197     }
<a name="l00198"></a>00198   }
<a name="l00199"></a>00199 
<a name="l00200"></a>00200   <span class="comment">// data members</span>
<a name="l00201"></a>00201   FeatureVector&lt;int,double&gt; weights_;
<a name="l00202"></a>00202   FeatureVector&lt;int,double&gt; average_weights_;
<a name="l00203"></a>00203   FeatureVector&lt;int,double&gt; weight_sums_;
<a name="l00204"></a>00204   FeatureVector&lt;int,int&gt; last_update_indices_;
<a name="l00205"></a>00205 };
<a name="l00206"></a>00206 
<a name="l00207"></a>00207 }  <span class="comment">// namespace reranker</span>
<a name="l00208"></a>00208 
<a name="l00209"></a>00209 <span class="preprocessor">#endif</span>
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